• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.59-66
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• 2024

This paper presents the concept and characteristics of cloud computing, and it addresses how cloud computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one's workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. This paper then looks at the primary goal of this project, which is to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In this survey, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies and those that used in this field. The paper concludes with a summary of the literature on predicting workloads and optimising QoS in the cloud computing.

• Journal of Convergence Society for SMB
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• v.5 no.1
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• pp.31-35
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• 2015

Cloud computing is internet-based computing technology. This is a form for exchanging service focused on the Internet. Because Cost is saved and use is easy there's a tendency that many companies are using. Cloud is in the form of a public cloud and private cloud and hybrid cloud. The service model is SaaS, PaaS, IaaS. Cloud computing use is simple but it has a security vulnerability. In particular, there is a vulnerability in virtualization and centralized information. In order to overcome this new security technology is to be developed. In particular, network security technology and authentication technology should be developed. Another way to overcome security responsibilities must be clearly and policies should be unified.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.511-521
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• 2022

Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.

• Electronics and Telecommunications Trends
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• v.30 no.1
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• pp.133-143
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• 2015

최근 기술발전으로 컴퓨터 수준의 스마트 디바이스를 이용한 무선 인터넷 서비스 이용이 확대되고 있어 이에 따른 트래픽 증가를 효율적으로 수용하기 위한 새로운 이동통신시스템과 네트워크 구조 연구가 활발하게 진행되고 있다. 이러한 과정에 유선 IT 분야에서 선행적으로 진행된 SDN(Software Defined Network) 및 cloud & virtualization 기술들을 이동통신의 액세스 또는 서비스 플랫폼 환경에 적용하는 선도적인 연구개발이 진행되고 있어 SDN 및 cloud & virtualization 분야에 대한 최근 동향을 파악하고 이러한 기술들이 이동통신분야에 적용되는 다양한 사례들을 분석하여 새로운 형태의 네트워크 및 시스템 구조와 방식에 대한 연구개발 방향을 제시한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1482-1484
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• 2019

We present a method for transmitting 3D object information in real time in a telepresence system. Three-dimensional object information consists of a large amount of point cloud data, which requires high performance computing power and ultra-wideband network transmission environment to process and transmit such a large amount of data in real time. In this paper, multiple users can transmit object motion and facial expression information in real time even in small network bands by using GANs (Generative Adversarial Networks), a non-supervised learning machine learning algorithm, for real-time transmission of 3D point cloud data. In particular, we propose the creation of an object similar to the original using only the feature information of 3D objects using conditional GANs.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.276-282
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• 2022

One of the most essential foundations upon which big institutions rely in delivering cloud computing and hosting services, as well as other kinds of multiple digital services, is the security of infrastructures for digital and information services throughout the world. Distributed denial-of-service (DDoS) assaults are one of the most common types of threats to networks and data centers. Denial of service attacks of all types operates on the premise of flooding the target with a massive volume of requests and data until it reaches a size bigger than the target's energy, at which point it collapses or goes out of service. where it takes advantage of a flaw in the Transport Control Protocol's transmitting and receiving (3-way Handshake) (TCP). The current study's major focus is on an architecture that stops DDoS attacks assaults by producing code for DDoS attacks using a cloud controller and calculating Round-Tripe Time (RTT).

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.233-239
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• 2022

Recently, research is being conducted on the rapid service provision and reliability of the instance-based rather than the existing IP-based structure. Research is mainly conducted through Block cloud, a platform that combines service-centric networking (SCN) and blockchain. In addition, the Internet of Things network has been proposed as a fog computing environment in the structure of the existing cloud computing. Fog computing is an environment suitable for real-time information processing. In this paper, we propose a new Internet network structure based on fog computing that requires real-time for rapid processing of IoT services. The proposed system applies IoTA, the third-generation blockchain based on DAG, to the block cloud. In addition, we want to propose a basic model of the object block chain and check the application services of electric vehicles.

• Advances in nano research
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• v.15 no.6
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• pp.513-520
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• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

• Journal of Internet Technology
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• v.19 no.6
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• pp.1741-1752
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• 2018

The best known software developers all offer cloud storage services. Microsoft offers Onedrive to its users, Apple offers iCloud Drive and Google offers Google Drive or GDrive. The battle between these software developers is ongoing and they will always strive to give the best services to their users. It is not only technology that is evolving, however, but also ways in which security can be breached and data abused. The security of information on the Internet is increasingly at risk and there are many threats to cloud storage platforms. This research used the mobile forensics approach to help in identifying and analyzing user behavior that may occur while using GDrive application for cybercrime. The novelty of comparison and analyzing methods performed in this research can help to find remnant data from all activities performed by GDrive users in Android smartphones. Hence, this proposed method can assist investigators in finding remnant data on GDrive client and can provide knowledge for legal practitioners.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.16-18
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• 2021

미디어 기술은 사용자가 더욱 몰입감을 느낄 수 있는 방향으로 개발되어 왔다. 이러한 흐름에 따라 기존의 2D 이미지에 비해 깊이감을 느낄 수 있는 증강 현실, 가상 현실 등 3D 공간 데이터를 활용하는 미디어가 주목을 받고 있다. 포인트 클라우드는 수많은 3차원 좌표를 가진 여러 개의 점들로 구성된 데이터 형식이므로 각각의 점들에 대한 좌표 및 색상 정보를 사용하여 3D 미디어를 표현한다. 고정된 크기의 해상도를 갖는 2D 이미지와 다르게 포인트 클라우드는 포인트의 개수에 따라 용량이 유동적이며, 이를 기존의 비디오 코덱을 사용하여 압축하기 위해 국제 표준기구인 MPEG(Moving Picture Experts Group)에서는 Video-based Point Cloud Compression (V-PCC)을 제정하였다. V-PCC는 3D 포인트 클라우드 데이터를 직교 평면 벡터를 이용하여 2D 패치로 분해하고 이러한 패치를 2D 이미지에 배치한 다음 기존의 2D 비디오 코덱을 사용하여 압축한다. 본 논문에서는 앞서 설명한 2D 패치 이미지에 super resolution network를 적용함으로써 3D 포인트 클라우드의 성능 향상하는 방안을 제안한다.